Summary of Evidence-enhanced Triplet Generation Framework For Hallucination Alleviation in Generative Question Answering, by Haowei Du et al.
Evidence-Enhanced Triplet Generation Framework for Hallucination Alleviation in Generative Question Answering
by Haowei Du, Huishuai Zhang, Dongyan Zhao
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel evidence-enhanced triplet generation framework, EATQA, is proposed to address the issue of hallucination in generative question answering (GQA). The model predicts all combinations of (Question, Evidence, Answer) triplets by flipping source pairs and target labels. This framework bridges the distribution gap to distill knowledge from evidence during inference stage. The result ensures the model learns logical relationships between query, evidence, and answer, improving evidence generation and query answering. EATQA outperforms other LLMs-based methods and hallucination mitigation approaches on two challenging GQA benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EATQA is a new way to help computers answer questions using information from documents. Sometimes these answers are not actually in the document, which is called “hallucination”. The goal is to make sure the computer only gives answers that can be found in the text. To do this, EATQA predicts all possible combinations of question, evidence, and answer triplets. This helps the computer understand how questions relate to evidence and answers. The result is a more accurate and trustworthy way for computers to answer questions. |
Keywords
» Artificial intelligence » Hallucination » Inference » Question answering
